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<h1>Reviewer Instructions for AISTATS 2018</h1>

Reviews must be entered electronically through the 
<a href="https://cmt.research.microsoft.com/AISTATS2018/">CMT system for AISTATS 2017</a>.


<h3>Review Content</h3>
<p>
Each review should begin with a paragraph providing an overview of the paper, and summarizing its main contributions. In particular, some thought should be given to how the paper fits with the aims and topics of the conference (not interpreted overly narrowly). The paragraph should relate the ideas in the paper to to previous work in the field.
</p>
<p>
The next section of the review should deal with major comments, issues that the reviewer sees as standing in the way of acceptance of the paper, or issues that should be addressed prior to publication, or reasons for rejecting the paper.
</p>
<p>
The final section of the review should deal with any minor issues, such as typographical errors, spelling mistakes, or areas where presentation could be improved.
</p>
<p>
As was done last year, reviewers may request public or non-proprietary code/data as part of the initial reviews for the purpose of better judging the paper.  The authors will then provide the code/data as part of the author response.  This might be, for instance, to check whether the authors' methods work as claimed, or whether it correctly treats particular scenarios the authors did not consider in their initial submission.  Note this request is NOT to be used to ask the authors to release their code after the paper has been published.  Code/data should only be requested in the event that this is the deciding factor in paper acceptance. The request should be reasonable in light of the duration of the discussion period, which limits the time available for review.  The SPC member in charge of the paper will confirm whether a code/data request is warranted and reasonable. Authors may only submit separate code and data at the invitation of a reviewer; otherwise, the usual restrictions apply on author response length. The conference chairs will enable the anonymous transfer of code and data to the relevant reviewers.
</p>

<h3>Evaluation Criteria</h3>
<p>
Contributions of AISTATS papers can be categorized into four areas a) algorithmic, b) theoretical, c) unifying or d) application.
</p>
<p>
Algorithmic contributions may make a particular approach feasible for the first time or may extend the applicability of an approach (for example allowing it to be applied to very large data sets).
</p>
<p>
A theoretical contribution should provide a new result about a model or algorithm. For example convergence proofs, consistency proofs or performance guarantees.
</p>
<p>
A unifying contribution may bring together several apparently different ideas and show how they are related, providing new insights and directions for future research.
</p>
<p>
Finally, an application contribution should typically have aspects that present particular statistical challenges which require solution in a novel way or through clever adaptation of existing techniques.
</p>
<p>
A paper may exhibit one or more of these contributions, where each of them are important in advancing the state of the art in the field. Of course, at AISTATS we are also particularly keen to see work which relates machine learning and statistics or highlights novel connections between the fields or even contrasts them.
</p>
<p>
One aspect of result presentation that is often neglected is a discussion of the failure cases of an algorithm, often due to concern that reviewers will penalize authors who provide this information. We emphasize that description of failure cases as well as successes should be encouraged and rewarded in submissions.
</p>
<p>
When reviewing, bear in mind that one of the most important aspects of a successful conference paper is that it should be thought provoking. Thought provoking papers sometimes generate strong reactions on initial reading, which may sometimes be negative. However, if the paper genuinely represents a paradigm shift it may take a little longer than a regular paper to come around to the author's way of thinking. Keep an eye out for such papers, although they may take longer to review, if they do represent an important advance the effort will be well worth it.
</p>
<p>
Finally, we would like to signal to newcomers to AISTATS (and to machine-learning conferences generally) that the review process is envisioned in exactly the same spirit as in a top quality journal like JRSS B, JASA, or Annals of Statistics. Accepted contributions are published in proceedings, and acceptance is competitive, so authors can rightly include these contributions in their publication list, on par with papers published in top quality journals. Further, AISTATS does not give the option to revise and resubmit: if a paper cannot be accepted with minor revisions (e.g., as proposed by the authors in their response to the reviews), it should be rejected.
</p>
<p>
Given the culture gap between the statistics and machine learning communities, we thus want to emphasize from the start the required levels of quality and innovation. All deadlines are very strict, as we cannot delay an overall tight schedule.
</p>

<h3>Confidentiality and Double Blind Process</h3>

<p>
AISTATS 2017 is a double blind reviewed conference. Whilst we expect authors to remove information that will obviously reveal their identity, we also trust reviewers not to take positive steps to try and uncover the authors' identity.
</p>
<p>
We are happy for authors to submit material that they have placed online as tech reports (such as in arXiv), or that they have submitted to existing workshops that do not produce published proceedings. This can clearly present a problem with regard to anonymization. Please do not seek out such reports on line in an effort to deanonymize.
</p>
<p>
The review process is double blind. Authors do not know reviewer identities, and this includes any authors on the senior program committee (i.e., the area chairs). However, area chairs do see reviewer identities. Also, during the discussion phase reviewer identities will be made available to other reviewers. In other words, whilst the authors will not know your identity, your co-reviewers will. This should help facilitate discussion of the papers.
</p>
<p>
If a reviewer requests code from the authors, this code should be anonymized (e.g., author names should be removed from the file headers). That said, we understand that it might be difficult to remove all traces of the authors from the files, and will exercise reasonable judgment if innocent mistakes are made.
</p>
<p>
The AISTATS reviewing process is confidential. By agreeing to review you agree not to use ideas, results, code, and data from submitted papers in your work. This includes research and grant proposals. This applies unless that work has appeared in other publicly available formats, for example technical reports or other published work. You also agree not to distribute submitted papers, ideas, code, or data to anyone else. If you request code and accompanying data, you agree that this is provided for your sole use, and only for the purposes of assessing the submission. All code and data must be discarded once the review is complete, and may not be used in further research or transferred to third parties.
</p>

<h3>The CMT Reviewing System</h3>

<p>The first step in the review process is to enter conflicts of interests. These conflicts can be entered as domain names (e.g., cmu.edu) and also by marking specific authors with whom you have a conflict. The use of double blind reviewing means you may not able to determine the papers you have a conflict with, so it is important you go through this list carefully and mark any conflicts. You should mark a conflict with anyone who is or ever was your student or mentor, is a current or recent colleague, or is a close collaborator. If in doubt, it's probably better to mark a conflict, in order to avoid the appearance of impropriety. Your own username should be automatically marked as a conflict, but sometimes the same person may have more than one account, in which case you should definitely mark your other accounts as a conflict as well. If you do not mark a conflict with an author, it is assumed that you do not have a conflict by default.
</p>
<p>
CMT also requests subject information which will be used to assist allocation of reviewers to papers. Please enter relevant keywords to assist in paper allocation.
</p>
<p>
You can revise your review multiple times before the submission. Your formal invite to be a reviewer will come from the CMT system. The email address used in this invite is your login, you can change your password with a password reset from the login screen.
</p>

<h3>Supplementary Material</h3>
<p>
Supplementary material is allowed by AISTATS 2017. For example, this supplementary material could include proofs, video, source code or audio. As a reviewer you should feel free to make use of this supplementary material to help in your review, though reviewing supplementary material is up to your discretion.  One exception is the letter of revision from papers previously submitted to NIPS.  If this letter is present in the supplementary material, we ask you to take it into consideration.
</p>

<h3>Simultaneous Submission</h3>
<p>
Simultaneous submission to other conference venues in the areas of machine learning and statistics is not permitted.
</p>
<p>
Simultaneous submission to journal publications of significantly extended versions of the paper is permitted, as long as the publication date of the journal is not before May 2017.
</p>

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